Evolution and Application of Neural Networks in High- Dimensional Data Collaborative Filtering and Piano Performance Evaluation Systems

Since the traditional deep neural network cannot provide accurate serialized recommendation in the process of high-dimensional data collaborative filtering recommendation, thus this paper adds the attention mechanism to the traditional deep neural network to optimize the model structure, and proposes the data recommendation algorithm based on the improved deep neural network.Combined with the index performance comparison between the improved algorithm and the classical serialized recommendation algorithm, the accuracy of the improved deep neural network in high-dimensional data recommendation is verified.In order to realize the multifaceted evaluation of piano playing, pitch, duration, fingering, depth, and muscle stiffness evaluation indexes are proposed.Utilizing the computational volume advantage of the deep separable convolutional algorithm and equipped with the SE Block Attention Module, the multidimensional data generated during piano playing is analyzed.The combined recognition rate of piano playing gestures (down-finger, through-finger, across-finger, expanding and retracting) by the neural network model utilizing the deep separable convolutional network architecture + SE Block attention mechanism is 93.54%, which meets the requirements of the piano playing evaluation system.